{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f5c97f0",
   "metadata": {},
   "source": [
    "|序号|任务详情|\n",
    "|--|--|\n",
    "|任务1|图属性与图构造|\n",
    "|任务2|图查询与遍历|\n",
    "|任务3|节点中心性与应用|\n",
    "|任务4|图节点嵌入算法（DeepWalk/node2vec）|\n",
    "|任务5|图节点嵌入算法：LINE/SDNE|\n",
    "|任务6|图节点嵌入算法：GraphGAN|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8c93606",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import jieba\n",
    "from collections import Counter\n",
    "from gensim.models import Word2Vec\n",
    "import random\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "import joblib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70243900",
   "metadata": {},
   "source": [
    "# 任务1：图属性与图构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1cf3810-7c33-4abf-b928-65ac55f3b1d6",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 两列，分别为节点id，节点类别\n",
    "group = pd.read_csv('data/group.csv', header=None)\n",
    "\n",
    "# 两列，分别为出发节点id，目的节点id\n",
    "graph = pd.read_csv('data/graph.csv', header=None)\n",
    "group.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0978d3ad",
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   "outputs": [
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     "execution_count": 6,
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    "group"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18f0d1ce",
   "metadata": {},
   "outputs": [
    {
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       "4      1397   750\n",
       "...     ...   ...\n",
       "17977  1573  1573\n",
       "17978   402   402\n",
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "graph"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2bab6081",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 两列，分别为节点id，节点类别\n",
    "group = pd.read_csv('http://mirror.coggle.club/dataset/graph-wiki/group.txt.zip', sep='\\t', header=None)\n",
    "group.to_csv('data/group.csv', index=False)\n",
    "# 两列，分别为出发节点id，目的节点id\n",
    "graph = pd.read_csv('http://mirror.coggle.club/dataset/graph-wiki/graph.txt.zip', sep='\\t', header=None)\n",
    "graph.to_csv('data/graph.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d2d182b3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NetworkXError",
     "evalue": "random_state_index is incorrect",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mIndexError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\utils\\decorators.py\u001B[0m in \u001B[0;36m_random_state\u001B[1;34m(func, *args, **kwargs)\u001B[0m\n\u001B[0;32m    395\u001B[0m         \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 396\u001B[1;33m             \u001B[0mrandom_state_arg\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0margs\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mrandom_state_index\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    397\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mIndexError\u001B[0m: tuple index out of range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mNetworkXError\u001B[0m                             Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-8-9f967b79e857>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[1;31m# 只添加前100条边\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m \u001B[0mg\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0madd_edges_from\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mgraph\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;36m100\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 5\u001B[1;33m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw_spring\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mg\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      6\u001B[0m \u001B[1;31m# 添加所有数据\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      7\u001B[0m \u001B[0mg\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mDiGraph\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py\u001B[0m in \u001B[0;36mdraw_spring\u001B[1;34m(G, **kwargs)\u001B[0m\n\u001B[0;32m   1164\u001B[0m        \u001B[0mfunction\u001B[0m\u001B[1;33m.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1165\u001B[0m     \"\"\"\n\u001B[1;32m-> 1166\u001B[1;33m     \u001B[0mdraw\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mG\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mspring_layout\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mG\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1167\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1168\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\decorator.py\u001B[0m in \u001B[0;36mfun\u001B[1;34m(*args, **kw)\u001B[0m\n\u001B[0;32m    229\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mkwsyntax\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    230\u001B[0m                 \u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkw\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfix\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkw\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 231\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mcaller\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfunc\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mextras\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0margs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    232\u001B[0m     \u001B[0mfun\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    233\u001B[0m     \u001B[0mfun\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__doc__\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__doc__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\utils\\decorators.py\u001B[0m in \u001B[0;36m_random_state\u001B[1;34m(func, *args, **kwargs)\u001B[0m\n\u001B[0;32m    398\u001B[0m             \u001B[1;32mraise\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mNetworkXError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"random_state_index must be an integer\"\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    399\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0mIndexError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 400\u001B[1;33m             \u001B[1;32mraise\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mNetworkXError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"random_state_index is incorrect\"\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    401\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    402\u001B[0m         \u001B[1;31m# Create a numpy.random.RandomState instance\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mNetworkXError\u001B[0m: random_state_index is incorrect"
     ]
    }
   ],
   "source": [
    "# 创建图\n",
    "g = nx.DiGraph()\n",
    "# 只添加前100条边\n",
    "g.add_edges_from(graph.values[:100])\n",
    "nx.draw_spring(g)\n",
    "# 添加所有数据\n",
    "g = nx.DiGraph()\n",
    "g.add_edges_from(graph.values[:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f6ca95",
   "metadata": {},
   "source": [
    "# 任务2：图查询与遍历"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed90717b-18c0-4aa8-8644-360f553a24c5",
   "metadata": {},
   "source": [
    "- 广度优先：从根节点开始，一层一层向下遍历\n",
    "- 深度优先：从根节点开始，直接遍历第一个分支最深处，再逐一返回查看是否有其他更深的支节点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47f404cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "边个数： 16523\n",
      "点个数： 2405\n",
      "度均值： 13.740540540540541\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[1573, 2337, 489, 708, 1525, 1397],\n",
       " [1573, 280, 696, 1610, 1445, 1397],\n",
       " [1573, 445, 2395, 750, 1397],\n",
       " [1573, 933, 1455, 1401, 1412, 1397]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 边个数 节点个数\n",
    "print('边个数：',g.number_of_edges())\n",
    "print('点个数：', g.number_of_nodes())\n",
    "\n",
    "# 度均值\n",
    "\n",
    "print('度均值：',np.mean([x[1] for x in list(g.degree())]))\n",
    "\n",
    "# 对节点1397的深度5内进行深度和广度遍历\n",
    "# print(nx.dfs_tree(g, 1397, 5).nodes())\n",
    "# print(nx.bfs_tree(g, 1397, 5).nodes())\n",
    "\n",
    "# 节点1573与节点1397之间的路径\n",
    "list(nx.connectivity.node_disjoint_paths(g, 1573, 1397))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8806fe5-2630-43a7-861c-82e51abd94b7",
   "metadata": {},
   "source": [
    "# 任务3：节点中心性与应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "246f6da7-c85c-40dc-b834-ff574b298443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1615, 1616, 713, 2158, 1624, 2165, 2167, 1630, 1631, 1640, 1659, 1662, 2280, 2307]\n"
     ]
    },
    {
     "ename": "NetworkXError",
     "evalue": "random_state_index is incorrect",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mIndexError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\utils\\decorators.py\u001B[0m in \u001B[0;36m_random_state\u001B[1;34m(func, *args, **kwargs)\u001B[0m\n\u001B[0;32m    395\u001B[0m         \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 396\u001B[1;33m             \u001B[0mrandom_state_arg\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0margs\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mrandom_state_index\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    397\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mIndexError\u001B[0m: tuple index out of range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mNetworkXError\u001B[0m                             Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-9-f54578ea8744>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      7\u001B[0m     \u001B[0mselected_nodes\u001B[0m \u001B[1;33m+=\u001B[0m \u001B[0mlist\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdfs_tree\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mg\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnode\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mnodes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      8\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mselected_nodes\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 9\u001B[1;33m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdraw_networkx\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mg\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msubgraph\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mselected_nodes\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py\u001B[0m in \u001B[0;36mdraw_networkx\u001B[1;34m(G, pos, arrows, with_labels, **kwds)\u001B[0m\n\u001B[0;32m    331\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    332\u001B[0m     \u001B[1;32mif\u001B[0m \u001B[0mpos\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 333\u001B[1;33m         \u001B[0mpos\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdrawing\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mspring_layout\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mG\u001B[0m\u001B[1;33m)\u001B[0m  \u001B[1;31m# default to spring layout\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    334\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    335\u001B[0m     \u001B[0mdraw_networkx_nodes\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mG\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpos\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mnode_kwds\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\decorator.py\u001B[0m in \u001B[0;36mfun\u001B[1;34m(*args, **kw)\u001B[0m\n\u001B[0;32m    229\u001B[0m             \u001B[1;32mif\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[0mkwsyntax\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    230\u001B[0m                 \u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkw\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfix\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mkw\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 231\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mcaller\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfunc\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mextras\u001B[0m \u001B[1;33m+\u001B[0m \u001B[0margs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkw\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    232\u001B[0m     \u001B[0mfun\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__name__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    233\u001B[0m     \u001B[0mfun\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__doc__\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m__doc__\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\env\\Anaconda3\\lib\\site-packages\\networkx\\utils\\decorators.py\u001B[0m in \u001B[0;36m_random_state\u001B[1;34m(func, *args, **kwargs)\u001B[0m\n\u001B[0;32m    398\u001B[0m             \u001B[1;32mraise\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mNetworkXError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"random_state_index must be an integer\"\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    399\u001B[0m         \u001B[1;32mexcept\u001B[0m \u001B[0mIndexError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 400\u001B[1;33m             \u001B[1;32mraise\u001B[0m \u001B[0mnx\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mNetworkXError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"random_state_index is incorrect\"\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    401\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    402\u001B[0m         \u001B[1;31m# Create a numpy.random.RandomState instance\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mNetworkXError\u001B[0m: random_state_index is incorrect"
     ]
    }
   ],
   "source": [
    "# 筛选度最大的Top10个节点，并对节点深度1以内的节点进行可视化；\n",
    "g_degree = pd.DataFrame(g.degree()).sort_values(by=1)\n",
    "g_degree = g_degree.iloc[-1:] # 模拟单个节点\n",
    "\n",
    "selected_nodes = []\n",
    "for node in g_degree[0].values:\n",
    "    selected_nodes += list(nx.dfs_tree(g, node, 1).nodes())\n",
    "print(selected_nodes)\n",
    "nx.draw_networkx(g.subgraph(selected_nodes))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af08b91",
   "metadata": {},
   "source": [
    "pagerank: 使用出链入链的方式计算图中节点的重要值。如：很多网站指向google.com，则证明google.com比较重要\n",
    "- 优点：可以快速找到图中的重点\n",
    "- 缺点：由于依赖图之间连接关系，所以对新的节点不友好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3705c095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vertex_id</th>\n",
       "      <th>pagerank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16:2660bf85-1d30-4c28-9008-b7f011083b2f</td>\n",
       "      <td>0.000673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16:462b355e-9736-4118-a6be-ac3e1338b914</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 vertex_id  pagerank\n",
       "0  16:2660bf85-1d30-4c28-9008-b7f011083b2f  0.000673\n",
       "1  16:462b355e-9736-4118-a6be-ac3e1338b914  0.000614"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[\"16:2660bf85-1d30-4c28-9008-b7f011083b2f\",0.0006728095922711247],\n",
    "                  [\"16:462b355e-9736-4118-a6be-ac3e1338b914\", 0.00061363385564756],\n",
    "                  [\"12:e137a131-93c0-4732-ac1c-b55f7103c31d\", 0.0003765897249631866]], columns = ['vertex_id', 'pagerank'])\n",
    "df[df['vertex_id'].str.contains(\"16:\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "83001fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1398: 0.06104772086775263,\n",
       " 1: 0.01701300414470976,\n",
       " 445: 0.014395310291998609,\n",
       " 393: 0.013602955499398835,\n",
       " 696: 0.01298183916383392,\n",
       " 1713: 0.012831912932317542,\n",
       " 713: 0.012072336899724097,\n",
       " 489: 0.011280547159212515,\n",
       " 1601: 0.011150264045905758,\n",
       " 1597: 0.011150264045905758,\n",
       " 890: 0.011150264045905758,\n",
       " 1592: 0.011150264045905758,\n",
       " 226: 0.011019980932599004,\n",
       " 1615: 0.01076950576665654,\n",
       " 1630: 0.01076950576665654,\n",
       " 1550: 0.010759414705985492,\n",
       " 780: 0.010759414705985492,\n",
       " 2273: 0.010759414705985492,\n",
       " 181: 0.010748758593226252,\n",
       " 116: 0.010748758593226252,\n",
       " 68: 0.010748758593226252,\n",
       " 700: 0.010498848479371981,\n",
       " 695: 0.010498848479371981,\n",
       " 722: 0.010498848479371981,\n",
       " 714: 0.010498848479371981,\n",
       " 1470: 0.010498848479371981,\n",
       " 362: 0.010498848479371981,\n",
       " 750: 0.010498848479371981,\n",
       " 1462: 0.010498848479371981,\n",
       " 818: 0.010115610671085297,\n",
       " 889: 0.010115610671085297,\n",
       " 814: 0.010115610671085297,\n",
       " 1610: 0.010115610671085297,\n",
       " 734: 0.010115610671085297,\n",
       " 776: 0.010115610671085297,\n",
       " 718: 0.010115610671085297,\n",
       " 708: 0.010115610671085297,\n",
       " 1445: 0.010064571435016129,\n",
       " 458: 0.010064571435016129,\n",
       " 133: 0.010064571435016129,\n",
       " 1443: 0.010064571435016129,\n",
       " 63: 0.010064571435016129,\n",
       " 436: 0.010064571435016129,\n",
       " 100: 0.010064571435016129,\n",
       " 101: 0.010064571435016129,\n",
       " 107: 0.010064571435016129,\n",
       " 16: 0.009977716026144959,\n",
       " 92: 0.009977716026144959,\n",
       " 73: 0.009977716026144959,\n",
       " 66: 0.009977716026144959,\n",
       " 64: 0.009977716026144959,\n",
       " 172: 0.009977716026144959,\n",
       " 48: 0.009967059913385718,\n",
       " 11: 0.009967059913385718,\n",
       " 88: 0.009967059913385718,\n",
       " 119: 0.009967059913385718,\n",
       " 493: 0.009967059913385718,\n",
       " 167: 0.009967059913385718,\n",
       " 67: 0.009967059913385718,\n",
       " 2165: 0.009849912441875669,\n",
       " 2307: 0.009849912441875669,\n",
       " 1631: 0.009849912441875669,\n",
       " 2280: 0.009849912441875669,\n",
       " 1616: 0.009849912441875669,\n",
       " 1624: 0.009849912441875669,\n",
       " 2167: 0.009849912441875669,\n",
       " 2158: 0.009849912441875669,\n",
       " 1640: 0.009849912441875669,\n",
       " 1659: 0.009849912441875669,\n",
       " 1662: 0.009849912441875669,\n",
       " 220: 0.009717149799531448,\n",
       " 236: 0.009717149799531448,\n",
       " 1412: 0.009717149799531448,\n",
       " 1413: 0.009717149799531448,\n",
       " 228: 0.009717149799531448,\n",
       " 233: 0.009717149799531448,\n",
       " 235: 0.009717149799531448,\n",
       " 1450: 0.009717149799531448,\n",
       " 1466: 0.009717149799531448,\n",
       " 1455: 0.009717149799531448,\n",
       " 1465: 0.009717149799531448,\n",
       " 1468: 0.009717149799531448,\n",
       " 243: 0.009717149799531448,\n",
       " 245: 0.009717149799531448,\n",
       " 1567: 0.009196017346304425,\n",
       " 1535: 0.009196017346304425,\n",
       " 186: 0.009196017346304425,\n",
       " 1005: 0.009196017346304425,\n",
       " 15: 0.009196017346304425,\n",
       " 1401: 0.009196017346304425,\n",
       " 1397: 0.009196017346304425,\n",
       " 0: 0.009196017346304425}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_pagerank = pd.DataFrame(list(nx.pagerank(g).items()), columns=['vertex_id', 'pagerank'])\n",
    "g_pagerank = g_pagerank.sort_values(by='pagerank', ascending=False)\n",
    "g_pagerank.set_index('vertex_id')['pagerank'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b65a3ef-4dd6-4ac3-9bdf-e486b997b7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用PageRank筛选Top10个节点，并对节点深度1以内的节点进行可视化\n",
    "g_pagerank = pd.DataFrame.from_dict(nx.pagerank(g), orient='index')\n",
    "g_pagerank = g_pagerank.sort_values(by=0)\n",
    "\n",
    "g_pagerank = g_pagerank.iloc[-10:]\n",
    "selected_nodes = []\n",
    "for node in g_pagerank[0].index:\n",
    "    selected_nodes += list(nx.dfs_tree(g, node, 1).nodes())\n",
    "\n",
    "nx.draw_spring(g.subgraph(selected_nodes), node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7171b233-ad38-424e-8bd4-f6895b28e86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['32%', '140', '2016', '以来', '最低', '单季', '收入', '利润', '缩水', '净利润'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_1 = '一纸四季报，令芯片巨头英特尔一夜间股价重挫近6.5%，市值蒸发80亿美元，再度被AMD反超。这份严重缩水的财报显示，英特尔在去年四季度营收大降32%至140亿美元，是2016年以来最低单季收入；净利润由三季度的10.2亿美元转为近7亿美元净亏损；毛利率更从2021年四季度的53.6%大幅下降至39.2%。此番业绩“跳水”并非英特尔一家的一时失利，在全球PC出货量整体下滑的背景下，包括英特尔、AMD、英伟达、高通在内的芯片企业，均在过去一年里出现不同程度的收入与利润下滑，但英特尔的确是其中的重灾区。'\n",
    "text_2 = '2021年，成都地区生产总值已经超过1.99万亿元，距离2万亿门槛仅咫尺之间。在去年遭受多轮疫情冲击及高温限电冲击的不利影响下，2022年，成都市实现地区生产总值20817.5亿元，按可比价格计算，比上年增长2.8%。成都因此成为第7个跨过GDP2万亿门槛的城市。目前，GDP万亿城市俱乐部中形成了4万亿、3万亿、2万亿和万亿这四个梯队。上海和北京在2021年跨过了4万亿，深圳2021年跨过了3万亿，重庆、广州、苏州和成都则是2万亿梯队。在排名前十的城市中，预计武汉将超过杭州。武汉市政府工作报告称，预计2022年武汉地区生产总值达到1.9万亿元左右。而杭州市统计局公布的数据显示，杭州2022年地区生产总值为18753亿元。受疫情影响，武汉在2020年GDP排名退居杭州之后。'\n",
    "text_3 = '据报道，美国证券交易委员会（SEC）与特斯拉首席执行官埃隆·马斯克之间又起波澜。SEC正对马斯克展开调查，主要审查内容是，马斯克是否参与了关于特斯拉自动驾驶软件的不恰当宣传。据知情人士透露，该机构正在调查马斯克是否就驾驶辅助技术发表了不恰当的前瞻声明。特斯拉在2014年首次发布了其自动驾驶辅助功能，公司声称该功能可以让汽车在车道内自动转向、加速和刹车。目前所有特斯拉车辆都内置了该软件。'\n",
    "\n",
    "\n",
    "g2 = nx.Graph()\n",
    "words = jieba.lcut(text_1)\n",
    "words = [x for x in words if len(x) > 1]\n",
    "# 关联前后两个词\n",
    "for i in range(len(words)-2):\n",
    "    for j in range(i-2, i+2):\n",
    "        if i == j:\n",
    "            continue\n",
    "        g2.add_edge(words[i], words[j])\n",
    "# 画图统计\n",
    "g2_node_gree = dict(g2.degree())\n",
    "word_counter = dict(Counter(words))\n",
    "\n",
    "g2_node_gree = pd.DataFrame.from_dict(g2_node_gree, orient='index')\n",
    "g2_node_gree.columns = ['degree']\n",
    "\n",
    "g2_node_gree['freq'] = g2_node_gree.index.map(word_counter)\n",
    "g2_node_gree['score'] = g2_node_gree['degree'] / g2_node_gree['freq']\n",
    "\n",
    "g2_node_gree.sort_values(by='score').index[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c6dacf",
   "metadata": {},
   "source": [
    "TextRank算法其实就是文本的PageRank算法\n",
    "1. 将文本进行分词梳理\n",
    "2. 将上下文的词作为连接，并形成图数据\n",
    "3. 利用pagerank算法计算权重\n",
    "4. 得到关键字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54431314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['39.2%', '四季', '一纸', 'AMD', '下滑', '四季度', '收入', '芯片', '亿美元', '英特尔'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2 = nx.Graph()\n",
    "\n",
    "words = jieba.lcut(text_1)\n",
    "words = [x for x in words if len(x) > 1]\n",
    "for i in range(len(words)-2):\n",
    "    for j in range(i-2, i+2):\n",
    "        if i == j:\n",
    "            continue\n",
    "        g2.add_edge(words[i], words[j])\n",
    "\n",
    "g2_node_gree = pd.DataFrame.from_dict(nx.pagerank(g2), orient='index')\n",
    "g2_node_gree.columns = ['degree']\n",
    "g2_node_gree = g2_node_gree.sort_values(by='degree')\n",
    "g2_node_gree.index[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f73e6c28",
   "metadata": {},
   "source": [
    "# 任务4：图节点嵌入算法：DeepWalk/node2vec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d977d4",
   "metadata": {},
   "source": [
    "DeepWalk的思想类似word2vec\n",
    "- word2vec默认相邻的词存在联系\n",
    "- DeepWalk默认相邻的节点有联系 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee43520",
   "metadata": {},
   "source": [
    "node2vec和DeepWalk了类似，但node2vec是一种有权带偏的游走"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26624db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def deepwalk(G, walk_length):\n",
    "    nodes = G.nodes()\n",
    "    history_walks = []\n",
    "    # 对于每个节点\n",
    "    for node in nodes:\n",
    "        # 从当前节点开始\n",
    "        random_walk_length = [node]\n",
    "        # 开始游走\n",
    "        for i in range(walk_length-1):\n",
    "            # 找到节点邻居\n",
    "            neighbors = list(G.neighbors(node))\n",
    "            # 排除已经游走的邻居\n",
    "            neighbors = list(set(neighbors) - set(random_walk_length))    \n",
    "            if len(neighbors) == 0:\n",
    "                break\n",
    "            # 随机挑选邻居\n",
    "            random_neighbor = random.choice(neighbors)            \n",
    "            random_walk_length.append(str(random_neighbor))\n",
    "            # 从下一个邻居继续游走\n",
    "            node = random_neighbor\n",
    "        # 此节点的游走路径\n",
    "        history_walks.append(random_walk_length)\n",
    "    return history_walks\n",
    "\n",
    "g = nx.DiGraph()\n",
    "# 构件图\n",
    "g.add_edges_from(graph.values[:])\n",
    "# 游走\n",
    "history_walks = deepwalk(g, 100)\n",
    "# 训练word2vec\n",
    "w2v = Word2Vec(history_walks, vector_size=50)\n",
    "# 节点类别，因为word2vec有单独节点次序\n",
    "node_group = group.iloc[list(w2v.wv.key_to_index.keys())][1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5eaa410-a59c-4a2f-8850-e5f903ac7fd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\env\\Anaconda\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "E:\\env\\Anaconda\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15ad16c8310>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tsne = TSNE(n_components=2)\n",
    "tsne_data = tsne.fit_transform(w2v.wv.vectors)\n",
    "\n",
    "\n",
    "x = np.arange(20)\n",
    "ys = [i+x+(i*x)**2 for i in range(20)]\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "\n",
    "plt.scatter(\n",
    "    tsne_data[:, 0], \n",
    "    tsne_data[:, 1],\n",
    "    color=colors[node_group]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe3409d7-3205-455b-abb8-93f82465fd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6284584980237155"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 划分数据集\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(\n",
    "    w2v.wv.vectors, node_group, test_size=0.2, stratify=node_group\n",
    ")\n",
    "\n",
    "# 模型训练与验证，准确率0.535\n",
    "model = LogisticRegression()\n",
    "model.fit(x_tr, y_tr)\n",
    "model.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b4769b59-0282-4ec7-9a89-5d29ea61750e",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 2405/2405 [00:10<00:00, 237.42it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 2405/2405 [00:10<00:00, 233.82it/s]\n",
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     ]
    }
   ],
   "source": [
    "def next_step(graph, previous, current, p, q):\n",
    "    neighbors = list(graph.neighbors(current))\n",
    "\n",
    "    weights = []\n",
    "    # Adjust the weights of the edges to the neighbors with respect to p and q.\n",
    "    for neighbor in neighbors:\n",
    "        if neighbor == previous:\n",
    "            # Control the probability to return to the previous node.\n",
    "            weights.append(1 / p)\n",
    "        elif graph.has_edge(neighbor, previous):\n",
    "            # The probability of visiting a local node.\n",
    "            weights.append(1)\n",
    "        else:\n",
    "            # Control the probability to move forward.\n",
    "            weights.append(1 / q)\n",
    "\n",
    "    # Compute the probabilities of visiting each neighbor.\n",
    "    weight_sum = sum(weights)\n",
    "    probabilities = [weight / weight_sum for weight in weights]\n",
    "\n",
    "    if len(neighbors) == 0:\n",
    "        return None\n",
    "\n",
    "    # Probabilistically select a neighbor to visit.    \n",
    "    next = np.random.choice(neighbors, size=1, p=probabilities)[0]\n",
    "    return next\n",
    "\n",
    "\n",
    "def random_walk(graph, num_walks, num_steps, p, q):\n",
    "    walks = []\n",
    "    nodes = list(graph.nodes())\n",
    "    # Perform multiple iterations of the random walk.\n",
    "    for walk_iteration in range(num_walks):\n",
    "        random.shuffle(nodes)\n",
    "\n",
    "        for node in tqdm(nodes):\n",
    "            # Start the walk with a random node from the graph.\n",
    "            walk = [node]\n",
    "            # Randomly walk for num_steps.\n",
    "            while len(walk) < num_steps:\n",
    "                current = walk[-1]\n",
    "                previous = walk[-2] if len(walk) > 1 else None\n",
    "                # Compute the next node to visit.\n",
    "                next = next_step(graph, previous, current, p, q)\n",
    "                if next:\n",
    "                    walk.append(next)\n",
    "                else:\n",
    "                    break\n",
    "            # Add the walk to the generated sequence.\n",
    "            walks.append(walk)\n",
    "\n",
    "    return walks\n",
    "\n",
    "# Random walk return parameter.\n",
    "p = 1\n",
    "# Random walk in-out parameter.\n",
    "q = 4\n",
    "# Number of iterations of random walks.\n",
    "num_walks = 5\n",
    "# Number of steps of each random walk.\n",
    "num_steps = 100\n",
    "# 游走结果\n",
    "walks = random_walk(g, num_walks, num_steps, p, q)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d52dc543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5841995841995842"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练word2vec\n",
    "w2v = Word2Vec(walks, vector_size=50)\n",
    "# 节点类别，因为word2vec有单独节点次序\n",
    "node_group = group.iloc[list(w2v.wv.key_to_index.keys())][1].values\n",
    "\n",
    "# 划分数据集\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(\n",
    "    w2v.wv.vectors, node_group, test_size=0.2, stratify=node_group\n",
    ")\n",
    "\n",
    "# 模型训练与验证，准确率0.535\n",
    "model = LogisticRegression()\n",
    "model.fit(x_tr, y_tr)\n",
    "model.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61da02ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存embedding\n",
    "l = list(w2v.wv.key_to_index.keys())\n",
    "l.sort()\n",
    "emb_matrix = w2v.wv.vectors\n",
    "emb_result = {}\n",
    "for i in l:\n",
    "    emb_indx = list(w2v.wv.key_to_index.keys()).index(i)\n",
    "    emb_result[i] = emb_matrix[emb_indx]\n",
    "joblib.dump(emb_result, filename='data/node2vec_emb.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b1e4fb",
   "metadata": {},
   "source": [
    "# 任务5：图节点嵌入算法：LINE/SDNE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e9ace3-d0ca-4616-af19-d76b02236326",
   "metadata": {},
   "source": [
    "参考样例 https://gitee.com/hkstorm/GraphEmbedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ade4162-a3f1-453e-ab4e-d4d9f3630da9",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Roaming\\Python\\Python38\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:1961: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "97/97 - 0s - loss: 0.6931\n",
      "Epoch 2/20\n",
      "97/97 - 0s - loss: 0.6923\n",
      "Epoch 3/20\n",
      "97/97 - 0s - loss: 0.6869\n",
      "Epoch 4/20\n",
      "97/97 - 0s - loss: 0.6541\n",
      "Epoch 5/20\n",
      "97/97 - 0s - loss: 0.5628\n",
      "Epoch 6/20\n",
      "97/97 - 0s - loss: 0.4541\n",
      "Epoch 7/20\n",
      "97/97 - 0s - loss: 0.3928\n",
      "Epoch 8/20\n",
      "97/97 - 0s - loss: 0.3517\n",
      "Epoch 9/20\n",
      "97/97 - 0s - loss: 0.3311\n",
      "Epoch 10/20\n",
      "97/97 - 0s - loss: 0.3167\n",
      "Epoch 11/20\n",
      "97/97 - 0s - loss: 0.3050\n",
      "Epoch 12/20\n",
      "97/97 - 0s - loss: 0.2952\n",
      "Epoch 13/20\n",
      "97/97 - 0s - loss: 0.2914\n",
      "Epoch 14/20\n",
      "97/97 - 0s - loss: 0.2702\n",
      "Epoch 15/20\n",
      "97/97 - 0s - loss: 0.2618\n",
      "Epoch 16/20\n",
      "97/97 - 0s - loss: 0.2513\n",
      "Epoch 17/20\n",
      "97/97 - 0s - loss: 0.2406\n",
      "Epoch 18/20\n",
      "97/97 - 0s - loss: 0.2315\n",
      "Epoch 19/20\n",
      "97/97 - 0s - loss: 0.2282\n",
      "Epoch 20/20\n",
      "97/97 - 0s - loss: 0.2117\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\env\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5426195426195426"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ge import LINE,SDNE\n",
    "g = nx.DiGraph()\n",
    "# 构件图\n",
    "g.add_edges_from(graph.values[:])\n",
    "line_model = LINE(g,embedding_size=50,order='second') #init model,order can be ['first','second','all']\n",
    "line_model.train(batch_size=1024,epochs=20,verbose=2)# train model\n",
    "line_embeddings = line_model.get_embeddings()# get embedding vectors\n",
    "\n",
    "node_group = group.iloc[list(line_embeddings.keys())][1].values\n",
    "x = list(line_embeddings.values())\n",
    "# 划分数据集\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(x, node_group, test_size=0.2, stratify=node_group)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_tr, y_tr)\n",
    "model.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2fee064-e680-4db1-8f83-7c145cf58784",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\env\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "D:\\env\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2079d49fe80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tsne = TSNE(n_components=2)\n",
    "tsne_data = tsne.fit_transform(x)\n",
    "\n",
    "\n",
    "x = np.arange(20)\n",
    "ys = [i+x+(i*x)**2 for i in range(20)]\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "\n",
    "plt.scatter(\n",
    "    tsne_data[:, 0], \n",
    "    tsne_data[:, 1],\n",
    "    color=colors[node_group]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbb3e1b",
   "metadata": {},
   "source": [
    "# 任务6：图节点嵌入算法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc33aa48",
   "metadata": {},
   "source": [
    "模型训练见GraphGan_Paddle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e13c9b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "获取embdding矩阵: 100%|███████████████████████████████████████████████████████| 2405/2405 [00:00<00:00, 2405174.33it/s]\n",
      "获取embdding矩阵: 100%|████████████████████████████████████████████████████████████████████| 2405/2405 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "def read_graph_embedding(embed_path):\n",
    "    '''\n",
    "    读取节点embedding\n",
    "    :param emb_path: embedding路径\n",
    "    :return:\n",
    "    '''\n",
    "    embed = joblib.load(filename=embed_path)\n",
    "    result_embed = []\n",
    "    result_keys = []\n",
    "    for k, v in tqdm(embed.items(), desc='获取embdding矩阵'):\n",
    "        result_embed.append(v)\n",
    "        result_keys.append(k)\n",
    "    return result_keys, result_embed\n",
    "\n",
    "keys, g_emb = read_graph_embedding('result/generator.emb')\n",
    "keys, d_emb = read_graph_embedding('result/discriminator.emb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1f351565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "基于生成器emb的分数： 0.16632016632016633\n"
     ]
    }
   ],
   "source": [
    "node_group = group.iloc[keys][1].values\n",
    "x = list(g_emb)\n",
    "# 划分数据集\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(x, node_group, test_size=0.2, stratify=node_group)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_tr, y_tr)\n",
    "print('基于生成器emb的分数：',model.score(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4af50d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "基于鉴别器emb的分数： 0.41164241164241167\n"
     ]
    }
   ],
   "source": [
    "node_group = group.iloc[keys][1].values\n",
    "x = list(d_emb)\n",
    "# 划分数据集\n",
    "x_tr, x_val, y_tr, y_val = train_test_split(x, node_group, test_size=0.2, stratify=node_group)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(x_tr, y_tr)\n",
    "print('基于鉴别器emb的分数：',model.score(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dbe5fe70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\env\\Anaconda\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "E:\\env\\Anaconda\\lib\\site-packages\\sklearn\\manifold\\_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1933e71cca0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "tsne = TSNE(n_components=2)\n",
    "tsne_data = tsne.fit_transform(x)\n",
    "\n",
    "\n",
    "x = np.arange(20)\n",
    "ys = [i+x+(i*x)**2 for i in range(20)]\n",
    "colors = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "\n",
    "plt.scatter(\n",
    "    tsne_data[:, 0], \n",
    "    tsne_data[:, 1],\n",
    "    color=colors[node_group]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd9bdfd0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
